One Class per Named Entity Exploiting Unlabeled Text for Named Entity Recognition

One Class per Named Entity:

Exploiting Unlabeled Text for Named Entity Recognition Yingchuan Wong and Hwee Tou Ng

Department of Computer Science

National University of Singapore

3Science Drive2,Singapore117543

yingchuan.wong@,nght@.sg

Abstract

In this paper,we present a simple yet novel method

of exploiting unlabeled text to further improve

the accuracy of a high-performance state-of-the-

art named entity recognition(NER)system.The

method utilizes the empirical property that many

named entities occur in one name class -

ing only unlabeled text as the additional resource,

our improved NER system achieves an F1score of

87.13%,an improvement of1.17%in F1score and

a8.3%error reduction on the CoNLL2003En-

glish NER official test set.This accuracy places

our NER system among the top3systems in the

CoNLL2003English shared task.

1Introduction

The named entity recognition(NER)task involves identify-ing and classifying noun phrases into one of many semantic classes such as persons,organizations,locations,etc.NER systems can be built by supervised learning from a labeled data set.However,labeled data sets are expensive to prepare as it involves manual annotation efforts by humans.As unla-beled data is easier and cheaper to obtain than labeled data, exploiting unlabeled data to improve NER performance is an important research goal.

An empirical property of named entities is that many named entities occur in one name class only.For example, in the CoNLL2003English NER[Sang and Meulder,2003] training set,more than98%of named entity types have ex-actly one name class.This paper presents a novel yet sim-ple method of exploiting this empirical property of one class per named entity to further improve the NER accuracy of a high-performance state-of-the-art NER ing only unlabeled data as the additional resource,we achieved an F1 score of87.13%,which is an improvement of1.17%in F1 score and a8.3%error reduction on the CoNLL2003English NER official test set.This accuracy places our NER system among the top3systems in the CoNLL2003English shared task.

2System Description

The NER system we implemented is based on the maximum entropy framework similar to the MENE system of[Borth-wick,1999].Each name class is divided into4sub-classes: ,,,and.Since the CoNLL 2003English NER shared task data has4name classes(per-son,organization,location,and miscellaneous),there is a to-tal of17classes(4name classes4sub-classes+1not-a-name class)that a word can possibly be assigned to.

2.1Maximum Entropy

In the maximum entropy framework,the best model is the one that has the highest entropy while satisfying the constraints imposed.These constraints are derived from training data, expressing some relationship between features and class.It is unique,agrees with the maximum-likelihood distribution, and has the exponential form[Pietra et al.,1997]: